Machine Learning: Supervised Methods
The field of machine learning constitutes a modern approach to artificial intelligence. It is situated in between computer science, neuroscience, statistics, and robotics, with applications ranging all over science and engineering, medicine, economics, etc. Machine learning algorithms automate the process of learning, thus allowing prediction and decision making machines to improve with experience.
This lecture will cover a contemporary spectrum of supervised learning methods. All lecture material will be in English.
The course will use the inverted classroom concept. Students work through the relevant lecture material at home. The material is then consolidated in a 4 hours/week practical session.
Lecturers
Prof. Dr. Tobias GlasmachersLecturer |
(+49) 234-32-25558 tobias.glasmachers@ini.rub.de NB 3/27 |
Details
- Course type
- Lectures
- Credits
- 6 CP
- Term
- Summer Term 2019
Dates
- Lecture
-
Takes place
every week on Thursday from 10:00 to 14:00 in room IA 0/158-79 (PC-Pool 1).
First appointment is on 04.04.2019
Last appointment is on 11.07.2019
Requirements
The course requires basic mathematical tools from linear algebra, calculus, and probability theory. More advanced mathematical material will be introduced as needed. The practical sessions involve programming exercises in Python. Participants need basic programming experience. They are expected to bring their own devices (laptops).
Most of the lecture is based on the following video lectures: https://www.youtube.com/playlist?list=PLD63A284B7615313A (CC license). All material will be made available in a moodle course.
The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.
Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany
Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210